Skip to main content
SpringerLink
Log in

Socio-economic and Trip Characteristics Influencing the Travel Time Perception of Cyclists: A Case Study of a Residential Academic Campus

  • Original Article
  • Published:
Transportation in Developing Economies Aims and scope Submit manuscript

Abstract

The perceived travel time is one of the important trip characteristics used in the field of travel behavior research. This study analyses the socio-economic and the trip characteristics influencing the travel time perception of the cyclists in a residential academic campus. Two approaches were used to gather the relevant information from the daily commuting cyclists. First, the actual travel time was collected by manually noting down the starting and ending times of the trip as well as using the smartphone sensors. Second, a questionnaire survey was administered to obtain the perceived travel time, other travel parameters, socio-economic, and behavioral characteristics of the trip maker. Using the actual and the revealed data, the present study has evaluated the perception error ratio (PER) and the difference in the perceived and actual travel times, and classified the over and under perceived trip makers. Then the factors influencing the perception error of cyclists were identified. Model results revealed that the use of PER misclassifies the over and under perceived trip makers. Actual travel time and the income level were found to influence the perception error corresponding to both the under and over perceptions. It was also found that the activities of the trip maker, during the trip have a significant influence on the trip makers’ perception.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Parthasarathi P, Levinson D, Hochmair H (2013) Network structure and travel time perception. PLoS ONE 8(10):e77718

    Article  Google Scholar 

  2. Delclòs-Alió X, Marquet O, Miralles-Guasch C (2017) Keeping track of time: a smartphone-based analysis of travel time perception in a suburban environment. Travel Behav Soc 9:1–9

    Article  Google Scholar 

  3. Clark JE (1982) Modeling travelers’ perceptions of travel time. Transp Res Rec 890:7–11

    Google Scholar 

  4. González RM, Martínez-Budría E, Díaz-Hernández JJ, Esquivel A (2015) Explanatory factors of distorted perceptions of travel time in tram. Transp Res F Traffic Psychol Behav 30:107–114

    Article  Google Scholar 

  5. Indian Institute of Technology Guwahati, Assam, India. http://www.iitg.ac.in. Accessed 10 July 2018

  6. Sims D, Matthews SA, Bopp MJ, Rovniak LS, Poole E (2018) Predicting discordance between perceived and estimated walk and bike times among university faculty, staff, and students. Transportmetrica A Transp Sci 14(8):691–705

    Article  Google Scholar 

  7. Peer S, Knockaert J, Koster P, Verhoef ET (2014) Over-reporting vs. overreacting: commuters’ perceptions of travel times. Transport Res Part A Policy Pract 69:476–494

    Article  Google Scholar 

  8. Zhu X, Li Q, Chen . (2013) APT: accurate outdoor pedestrian tracking with smartphones. In: INFOCOM, 2013 Proceedings IEEE, pp 2508–2516

  9. Manzoni V, Maniloff D, Kloeckl K, Ratti C (2010) Transportation mode identification and real-time CO2 emission estimation using smartphones. In: SENSEable City Lab, Massachusetts Institute of Technology

  10. Abdulazim T, Abdelgawad H, Habib K, Abdulhai B (2013) Using smartphones and sensor technologies to automate collection of travel data. Transp Rese Rec J Transp Res Board 2383:44–52

    Article  Google Scholar 

  11. Wolf J (2006) Applications of new technologies in travel surveys. In: Travel survey methods: quality and future directions. Emerald Group Publishing Limited, pp 531–544

  12. Wang Z, He SY, Leung Y (2018) Applying mobile phone data to travel behaviour research: a literature review. Travel Behav Soc 11:141–155

    Article  Google Scholar 

  13. Martens K (2004) The bicycle as a feedering mode: experiences from three European countries. Transp Res Part D Transp Environ 9(4):281–294

    Article  Google Scholar 

  14. World Bank Help Desk for economies of different countries. https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries. Accessed 5 July 2018

  15. Houston D, Luong TT, Boarnet MG (2014) Tracking daily travel; Assessing discrepancies between GPS-derived and self-reported travel patterns. Transp Res Part C Emerg Technol 48:97–108

    Article  Google Scholar 

  16. Kockelman KM, Kalmanje S (2005) Credit-based congestion pricing: a policy proposal and the public’s response. Transp Res Part A Policy Pract 39(7–9):671–690

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    V. A. Bharat Kumar Anna & Mallikarjuna Chunchu

  2. Computer Science Department, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India

    Venkatesh Tamarapalli

Authors
  1. V. A. Bharat Kumar Anna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mallikarjuna Chunchu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Venkatesh Tamarapalli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. A. Bharat Kumar Anna.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anna, V.A.B.K., Chunchu, M. & Tamarapalli, V. Socio-economic and Trip Characteristics Influencing the Travel Time Perception of Cyclists: A Case Study of a Residential Academic Campus. Transp. in Dev. Econ. 8, 7 (2022). https://doi.org/10.1007/s40890-021-00144-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40890-021-00144-1

Keywords

Search

Navigation